System for providing dialogue guidance

ABSTRACT

Various aspects of the subject technology relate to a dialogue guidance system. The dialogue guidance system is configured to receive input data captured from a communication event among at least a first participant and a second participant. The input data may include one or more of text data, audio data, or video data. The dialogue guidance system is configured to identify, based on the input data, one of a sentiment or a disposition corresponding to the communication event, determine dialogue guidance for the first participant based on one of the sentiment or the disposition, and provide the dialogue guidance to the first participant.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation and claims the prioritybenefit of U.S. patent application Ser. No. 16/563,461 filed Sep. 6,2019, now U.S. Pat. No. 11,429,794, which claims the priority benefit ofU.S. provisional patent application 62/727,965 filed on Sep. 6, 2018,the contents of which are hereby expressly incorporated by reference intheir entirety.

1. Field of the Invention

The present invention relates to sentiment detection in dialogueguidance systems.

2. Description of the Related Art

Humans constantly engage in persuasive discourse across various media ofinteraction. It is often the case that parties engaged in persuasivediscourse are unaware of the internal motivations of other partiesparticipating in the discourse. In many cases, a party may not even beentirely aware of their own internal motivations. This unawareness ofbaseline motivations may cause participants to “talk past each other”and thus greatly reduce the efficiency of communication.

People often find it difficult to ascertain a sentiment or dispositionof listeners during presentations, arguments, and other types ofdiscourse. While training and practice can allow people to improve theirability to ascertain sentiment and/or dispositions, human-basedmethodologies are notoriously unreliable and often result in incorrectassessments. A presenter, speaker, or debater and the like incorrectlyassessing sentiments or dispositions of other participants to a dialoguecan result in ineffective framing and/or presenting of arguments,points, references, and other information.

It is with these observations in mind, among others, that aspects of thepresent disclosure were concerned and developed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-B illustrate exemplary operating environments in which systemsand methods of the disclosure may be deployed, according to someembodiments of the subject technology;

FIG. 2 is a block diagram depicting a system for sentiment detection anddialogue guidance, according to some embodiments of the subjecttechnology;

FIG. 3 is a flowchart of a method for detecting sentiment and guidingdialogue, according to some embodiments of the subject technology; and

FIG. 4 is a system diagram of an example computing system that mayimplement various systems and methods discussed herein, in accordancewith various embodiments of the subject technology.

DETAILED DESCRIPTION

Aspects of the present disclosure involve systems and methods fordetecting a sentiment of, for example, an audience and providingsentiment-based guidance for discourse such as argument or debate.

Dialogue participants, such as an audience or other dialogue recipient,may receive information (e.g., a presentation or dialogue) differentlybased on either or both of individual and group sentiment anddisposition. Generally, a presenter may realize increased success (e.g.,convincing an audience of a stance, informing an audience, etc.) whenmade aware of the sentiment and disposition of other dialogueparticipants. The presenter can adjust aspects of how ideas arepresented in response to participant sentiment and disposition. Further,the sentiment and disposition can be used to automatically adjustdialogue submitted by the presenter (e.g., via text based medium such asemail or message board, etc.) to conform to reader sentiment on eitheran individual (e.g., each reader receives a respectively adjusteddialogue) or group basis (e.g., all readers receive a tonally optimizeddialogue).

For example, some audiences may be sympathetic (or antagonistic orapathetic) to certain group interests (e.g., social justice, economicfreedom, etc.), contextual frameworks, and the like. Those in discoursewith such audiences may find it advantageous to adjust word choice,framing references, pace, duration, rhetorical elements, illustrations,reasoning support models, and other aspects of a respective dialogue. Insome cases, for example, it may be advantageous to engage in aninquisitive or deliberative form of dialogue, whereas in other cases(e.g., before other audiences) the same ideas and points may be morelikely to be successfully conveyed in a persuasive or negotiation formof dialogue.

However, it is often difficult for a human to accurately determine thesentiment or disposition of an audience. In some cases, a person may betoo emotionally invested in the content being conveyed. In other cases,it may be difficult to gauge sentiment and disposition due to audiencesize or physical characteristics of the space where the dialogue isoccurring (e.g., the speaker may be at an angle or the like to theaudience, etc.). A speaker may also be a poor judge of audiencesentiment and disposition, for whatever reason, and so likely tomisjudge or fail to ascertain the audience sentiment and disposition.

A three-phase process can be enacted to alleviate the above issues aswell as augment intra-human persuasion (e.g., dialogue, presentation,etc.). Premises and their reasoning interrelationships may first beidentified and, in some cases, communicated to a user. In a secondphase, a user or users may be guided toward compliance with particularpersuasive forms (e.g., avoidance of fallacies, non-sequiturs,ineffective or detrimental analogies, definition creep orover-broadening, etc.). In some examples, guidance can occur inreal-time such as in a presentational setting or keyed-in messaging andthe like. Further, in a third phase, guiding information can beaugmented and/or supplemented with visual and/or audio cues and otherinformation, such as social media and/or social network information,regarding members to a dialogue (e.g., audience members at apresentation and the like). It is with the second and third phases whichthe systems and methods disclosed herein are primarily concerned.

In some examples, static information such as, without imputinglimitation, demographic, location, education, work history, relationshipstatus, life event history, group membership, cultural heritage, andother information can be used to guide dialogue. In some examples,dynamic information such as, without imputing limitation, interactionhistory (e.g., with the user/communicator, regarding the topic, with theservice or organization associated with the dialogue, over the Internetgenerally, etc.), speed of interaction, sentiment of interaction, mentalstate during interaction (e.g., sobriety, etc.), limitations of themedium of dialogue (e.g., screen size, auditorium seating, etc.),sophistication of participants to the dialogue, various personalitytraits (e.g., aggressive, passive, defensive, victimized, etc.), searchand/or purchase histories, errors and/or argument ratings or historieswithin the corresponding service or organization, evidence cited in thepast by dialogue participants, and various other dynamic factors whichmay be used to determine dialogue guidance.

In particular, the above information may be brought to bear in amicro-sculpted real-time communication by, for example and withoutimputing limitation, determining changes to be made in colloquialisms,idioms, reasoning forms, evidence types or source, vocabulary orillustration choices, or sentiment language. The determined changes canbe provided to a user (e.g., a speaker, communicator, etc.) to increasepersuasiveness of dialogue by indicating more effective paths ofcommunication to achieving understanding by other dialogue participants(e.g., by avoiding triggers or pitfalls based on the above information).

In one example, visual and audio data of an audience can be processedduring and throughout a dialogue. The visual and audio data may be usedby Natural Language Processing (NLP) and/or Computer Vision (CV) systemsand services in order to identify audience sentiment and/or disposition.CV/NLP processed data can be processed by a sentiment identifyingservice (e.g., a trained deep network, a rules based system, aprobabilistic system, some combination of the aforementioned, or thelike) which may receive analytic support by a group psychological deeplearning system to identify sentiment and/or disposition of audiencemembers. In particular, the system can provide consistent and unbiasedsentiment identification based on large volumes of reference data.

Identified sentiments and/or dispositions can be used to select dialogueforms. For example, and without imputing limitation, dialogue forms canbe generally categorized as forms for sentiment-based dialogue and formsfor objective-based dialogue. Sentiment-based dialogue forms can includerules, lexicons, styles, and the like for engaging in dialogue (e.g.,presenting to) particular sentiments. Likewise, objective-based dialogueforms may include rules, lexicons, styles, and the like for engaging indialogue in order to achieve certain specified objectives (e.g.,persuade, inform, etc.). Further, multiple dialogue forms can beselected and exert more or less influence based on respective sentimentand/or objectives or corresponding weights and the like.

Selected dialogue forms may be used to provide dialogue guidance one ormore users (e.g., speakers or participants). For example, dialogueguidance may include restrictions (e.g., words, phrases, metaphors,arguments, references, and such that should not be used), suggestions(e.g., words, phrases, metaphors, arguments, references, and such thatshould be used), or other guidance. Dialogue forms may include, forexample and without imputing limitation, persuasion, negotiation,inquiry, deliberation, information seeking, Eristics, and others.

In some examples, dialogue forms may also include evidence standards.For example, persuasive form may be associated with a heightenedstandard of evidence. At the same time, certain detected sentiments ordispositions may be associated with particular standards of evidence orsource preferences. For example, a dialogue participant employed in ahighly technical domain, such as an engineer or the like, may bedisposed towards (e.g., find more persuasive) sources associated with aparticular credential (e.g., a professor from an alma mater), aparticular domain (e.g., an electrical engineering textbook), aparticular domain source (e.g., an IEEE publication), and the like. Insome examples, a disposition or sentiment may be associated withheightened receptiveness to particular cultural references and the like.Further, in cases where multiple dialogue forms interact or otherwiseare simultaneously active (e.g., where a speaker is attempting topersuade an audience determined by the sentiment identification systemto be disposed towards believing the speaker), an evidence standardbased on both these forms may be suggested to the speaker.

Likewise, dialogue forms may also include premise interrelationshipstandards. For example, threshold values, empirical support,substantiation, and other characteristics of premise interrelationshipsmay be included in dialogue forms. The premise interrelationshipstandards can be included directly within or associated with dialogueforms as rules, or may be included in a probabilistic fashion (e.g.,increasing likelihoods of standards, etc.), or via some combination ofthe two.

Dialogue forms can also include burden of proof standards. For example,and without imputing limitation, null hypothesis requirements,references to tradition, “common sense”, principles based on parsimonyand/or complexity, popularity appeals, default reasoning, extensionand/or abstractions of chains of reasoning (in some examples, includingratings and such), probabilistic falsification, pre-requisite premises,and other rules and/or standards related to burden of proof may beincluded in or be associated with particular dialogue forms.

Once one or more dialogue forms have been selected based on identifiedsentiment and/or disposition, the forms can be presented to a user(e.g., a speaker) via a user device or some such. In some examples, thedialogue forms can be applied to preexisting information such as awritten speech and the like. The dialogue forms can also enable strategyand/or coaching of the user.

FIG. 1A depicts an example of an operational environment 100 for asentiment detection and dialogue guidance system. A speaker 102 presentsto an audience 104 while receiving automated and dynamic presentationcoaching provided by the sentiment detection dialogue guidance system.

As speaker 102 presents to audience 104, an input capture system 112retrieves visual and audio data from members of audience 104 within acapture range. Here, the capture range is denoted by a dotted linetriangle. While the range is depicted as static, it is understood thatin some examples, such as where tracking of a particular audience memberor some such is needed, the range may be dynamic and/or include othersystems and subsystems to capture relevant input data.

Input capture system 112 includes a video capture device 108 and anaudio capture device 110. Input capture system 112 may be a dedicateddevice or, in some examples, a mobile device such as a smartphone andthe like with video and audio capture capability.

Audio and visual data captured by input capture system 112 can beprovided to a processing device 106. Processing device 106 may be alocal computer or may be a remotely hosted application or server.

Processing device 106 processes visual and audio data in order toprovide dialogue coaching data to speaker 102. In effect, a real-timepresentation (e.g., dialogue) coaching can be provided to speaker 102.This real-time coaching can dynamically change in response to sentimentand disposition changes of audience 104, either on a per member basis oras a whole, detected by input capture system 112.

FIG. 1B depicts an example of an operational environment 150 fordialogue guidance system 156. In comparison to operational environment100, operational environment 150 of FIG. 1B can be asynchronous andincludes guidance bespoke to individual participant sentiment anddisposition. For example, dialogue in operational environment 150 maytake place over email, a message board, instant message, voice overinternet protocol (VoIP), video conferencing, or other networkcommunication.

Presenter dialogue is transmitted from a computer 152 over network 155(e.g., the Internet, etc.) so that it can be received by participant A160A and/or participant B 160B. During transmission, dialogue guidancesystem 156 can determine sentiments and dispositions for participant A160A and participant B 160B and apply respective dialogue guidance toversions of presenter dialogue corresponding to each participant.Further, dialogue guidance system 156 can provide participant sentimentand disposition information back to computer 152 for a presenter toreview. In some examples, dialogue guidance system 156 can additionally,or instead, provide information related to dialogue guidance forrespective participants 160A-B in order to provide a presenter with arobust understanding of each dialogue participant mental state.

Here, dialogue participant A 160A and dialogue participant B 160B aredepicted as including a single person 154A and 154B respectively.However, it is understood that multiple people may be included as adialogue participant and that either or both of individual sentimentsand dispositions or aggregated group sentiments and dispositions can bedetermined and accounted for by dialogue guidance system 156.

Visual and audio data retrieved by computers 156A-B associated withrespective participants 160A-B can be processed by dialogue guidancesystem 156 in determining participant sentiment. Additionally, in someexamples, dialogue guidance system 156 can retrieve supplementalinformation related to participating people 154A-B over network 155 suchas social media, social network, message board history (or histories),and the like. Dialogue guidance system 156 may then utilize the visualand audio data along with any supplemental information to determinesentiments and dispositions, determine guidance, and apply the guidanceautomatically to the presenter dialogue to generate bespoke guideddialogue respective to each participant 160A-B and based on respectivesentiments and dispositions. This can be performed asynchronously or, inother words, provided to participants 160A-B at different times (e.g.,as a participant logs into a forum account, checks an email account,opens an instant message client, etc.).

FIG. 2 depicts a sentiment detection and dialogue guidance system 200.System 200 may be implemented as an application on a computing devicesuch as processing device 106. System 200 receives visual and audioinput in order to provide dialogue data (e.g., coaching data) to a uservia a smartphone, tablet, desktop, laptop, or other device.

A computer vision engine 202 receives visual data while a naturallanguage processing engine 204 receives audio data. In some examples,visual and audio data is transmitted directly from video and/or audiodevices. In some examples, visual and audio data can be preprocessed orprovided remotely, from stored files, or other sources.

Computer vision engine 202 and natural language processing engine 206respectively transmit prepared visual and audio data to a sentimentidentifier service 206. Prepared visual and audio data may, for example,include flags at various portions of the visual and audio data, includeclips or snapshots, isolated or extracted sources (e.g., for tracking aparticular audience member and the like), multiple channels based on oneor more feeds, or other transformations as may be used by sentimentidentifier 206 to identify audience sentiment and/or dispositions.

Sentiment identifier service 206 can determine a sentiment ordisposition of an audience at individual levels and/or at an aggregatedlevel based on the audio and visual data. In some examples, sentimentidentifier 206 can exchange data with a psychological deep learningsystem 214. Psychological deep learning system 214 may interconnect withsocial networks and media 216 to retrieve additional information on anaudience and/or individuals within the audience. For example,psychological deep learning system 214 can derive and/or explore asocial graph (e.g., generate a social topology and the like) associatedwith one or more audience members to supplement or complementinformation used by psychological deep learning system 214 in creationof various profiles.

Psychological deep learning system 214 can include general, specific, ormixed profiles generated by deep learning systems and the like. Theprofiles may assist sentiment identifier service 206 in determiningaudience sentiment and disposition based on visual cues (e.g., facialexpressions, etc.), audio cues (e.g., audible responses, etc.), and thelike.

Sentiment identifier service 206 transmits sentiment data to a dialogueform selector service 208. Dialogue form selector service 208 processesreceived sentiment data to retrieve rules, metrics, guidance, and/orrestrictions as discussed above. In some examples, dialogue formselector service 208 retrieves stored dialogue data (e.g., preparedspeeches, etc.) for applying selected dialogue forms.

Dialogue form selector service 208 transmits dialogue coaching data to auser device 210. User device 210 may be a computer, mobile device,smartphone, tablet, or the like. In some examples, rather than, or inaddition to, user device 210, dialogue coaching data may be transmittedto downstream processes or services. For example, applicationprogramming interface (API) endpoints may receive dialogue coaching datafor storage, model training, and other processes.

In some examples, dialogue coaching data includes prepared statements.In other examples, dialogue coaching data may provide rules, guidance,restrictions, metrics, and the like.

FIG. 3 depicts a method 300 for processing audio and visual data togenerate guidance information for a speaker. Audio and visual data for aparticipant to a dialogue are received and supplemental informationrelating to the participant is retrieved (operation 302). In someexamples, the audio and visual data may be provided as individualstreams such as from one or more respective cameras and one or morerespective microphones. In other examples, a single system may provide amulti-layered data stream including both audio and visual data. Thesupplemental information can be retrieved, via API and the like, fromsocial media and/or social network platforms such as Twitter® orFacebook® and the like.

Audio and/or visual cues within the received audio and visual data alongwith the retrieved supplemental information are then used to identifyparticipant sentiment and/or disposition in response to a presenterdialogue (operation 304). For example, audience gaze, respiratoryresponse (e.g., gasps, sighs, etc.), and the like can may be associatedwith a sentiment. Machine learning models such as deep neural networks,regressions, support vector machines (SVMs), and other techniques may beused to identify sentiments.

The identified audience sentiment is then used to determine dialogueguidance (operation 306). Dialogue guidance can include restrictions,recommendations, and the like as discussed above. In some examples,dialogue guidance may include prepared statements, etc. The determinedguidance is then provided to the presenter (operation 308). In someexamples, such as where the dialogue takes place over a text medium likea forum and the like, the determined guidance can be automaticallyapplied to the dialogue in addition to, or rather than, providing theguidance to the presenter.

As seen in FIG. 3 , method 300 may repeat to provide continuous and/orstreaming dialogue guidance to a speaker. In some examples, dialogueguidance may include recommendations regarding semantic framing,references, lexicon and the like. In other examples, dialogue guidancemay include prepared comments to be read by the speaker (e.g., viasemantic transforms and other NLP processes). Additionally, where thedialogue is text based and different participants may receive thedialogue individually and independently, the dialogue may beautomatically modified according to determined guidance respective tosentiments and/or dispositions determined for each recipient at the timeof receipt such that one message from the presenting user could becustomized for each individual reader according to their state orsentiment at the time of their receipt, even if those receipt times andrecipient sentiments were different for the different recipients andeven though all the received messages might be deemed to have anequivalent persuasive effect (EPE). EPE can include anticipated levelsof impact upon or deflection to a belief held by a dialogue participant,tested responses to a corresponding subject matter of the dialogueparticipant (e.g., using before and after testing, A/B testing, etc.),physiological response tests (e.g., via brain scans, etc.), and the likewhich may provide further information to, for example, dialogue guidancesystem 156, for optimizing dialogue guidance.

Various aspects of the subject technology relate to a dialogue guidancesystem. The dialogue guidance system is configured to receive input datacaptured from a communication event among at least a first participantand a second participant. The communication event may include apresentation or other message or communication. The participants may be,for example, one or more presenters and one or more audience members orrecipients of the communication.

The input data may include one or more of text data, audio data, orvideo data. The dialogue guidance system is configured to identify,based on the input data, one of a sentiment or a dispositioncorresponding to the communication event, determine dialogue guidancefor the first participant based on one of the sentiment or thedisposition, and provide the dialogue guidance to one or more of theparticipants.

The dialogue guidance system may also be configured to retrievesupplemental information corresponding to at least one of the firstparticipant and the second participant, the supplemental informationincluding one or more of social media information, social networkinformation, or web platform information and the sentiment ordisposition may further be identified based on the supplementalinformation.

According to some aspects, the dialogue guidance system may include oneor more processors and at least one non-transitory computer-readablemedium having stored therein instructions which, when executed by theone or more processors, cause the dialogue guidance system to performoperations. The operations may include receiving, from an input capturesystem, input data associated with a presentation, identifying asentiment or a disposition corresponding to the presentation based onthe input data, determining dialogue guidance for a presenter of thepresentation, and providing the dialogue guidance to the presenter. Theinput data may be associated with the presenter or of one or moremembers of an audience of the presentation. Furthermore, the dialogueguidance may be provided to the presenter during the presentation orafter it.

A sentiment identifier service, a dialogue form selector service, orother services may also be leveraged by the dialogue guidance system.For example, the dialogue guidance system may transmit, over a networkto a sentiment identifier service, a query for the sentiment ordisposition or transmit, over a network to a dialogue form selectorservice, a query for the dialogue guidance.

Aspects of the subject technology also relate to a method for providingdialogue guidance. The method may include receiving input dataassociated with a dialogue participant, the input data comprising one ormore of text data, audio data, or video data, identifying one of asentiment or a disposition corresponding to the dialogue participantbased on the input data, determining, based on one of the sentiment orthe disposition, dialogue guidance for a presenter, and providing thedialogue guidance to the presenter. The dialogue participant may be amember of an audience or the presenter. The identifying of the sentimentor disposition is based on a deep learning system.

The determining of the dialogue guidance may include selecting at leastone dialogue form comprising a rule for communicating, wherein the atleast one dialogue form corresponds to the identified sentiment ordisposition. The rule for communicating may include restrictions,suggestions, or standards. The method may also include processing theinput data using at least one of a Natural Language Processing (NLP)and/or Computer Vision (CV) systems.

FIG. 4 is an example computing system 400 that may implement varioussystems and methods discussed herein. The computer system 400 includesone or more computing components in communication via a bus 402. In oneimplementation, the computing system 400 includes one or more processors404. The processor 404 can include one or more internal levels of cache406 and a bus controller or bus interface unit to direct interactionwith the bus 402. The processor 404 may specifically implement thevarious methods discussed herein. Main memory 408 may include one ormore memory cards and a control circuit (not depicted), or other formsof removable memory, and may store various software applicationsincluding computer executable instructions, that when run on theprocessor 404, implement the methods and systems set out herein. Otherforms of memory, such as a storage device 410 and a mass storage device418, may also be included and accessible, by the processor (orprocessors) 404 via the bus 402. The storage device 410 and mass storagedevice 418 can each contain any or all of the methods and systemsdiscussed herein.

The computer system 400 can further include a communications interface412 by way of which the computer system 400 can connect to networks andreceive data useful in executing the methods and system set out hereinas well as transmitting information to other devices. The computersystem 400 can also include an input device 416 by which information isinput. Input device 416 can be a scanner, keyboard, and/or other inputdevices as will be apparent to a person of ordinary skill in the art. Anoutput device 414 can be a monitor, speaker, and/or other output devicesas will be apparent to a person of ordinary skill in the art.

The system set forth in FIG. 4 is but one possible example of a computersystem that may employ or be configured in accordance with aspects ofthe present disclosure. It will be appreciated that other non-transitorytangible computer-readable storage media storing computer-executableinstructions for implementing the presently disclosed technology on acomputing system may be utilized.

In the present disclosure, the methods disclosed may be implemented assets of instructions or software readable by a device. Further, it isunderstood that the specific order or hierarchy of steps in the methodsdisclosed are instances of example approaches. Based upon designpreferences, it is understood that the specific order or hierarchy ofsteps in the methods can be rearranged while remaining within thedisclosed subject matter. The accompanying method claims presentelements of the various steps in a sample order, and are not necessarilymeant to be limited to the specific order or hierarchy presented.

The described disclosure may be provided as a computer program product,or software, that may include a computer-readable storage medium havingstored thereon instructions, which may be used to program a computersystem (or other electronic devices) to perform a process according tothe present disclosure. A computer-readable storage medium includes anymechanism for storing information in a form (e.g., software, processingapplication) readable by a computer. The computer-readable storagemedium may include, but is not limited to, optical storage medium (e.g.,CD-ROM), magneto-optical storage medium, read only memory (ROM), randomaccess memory (RAM), erasable programmable memory (e.g., EPROM andEEPROM), flash memory, or other types of medium suitable for storingelectronic instructions.

The description above includes example systems, methods, techniques,instruction sequences, and/or computer program products that embodytechniques of the present disclosure. However, it is understood that thedescribed disclosure may be practiced without these specific details.

While the present disclosure has been described with references tovarious implementations, it will be understood that theseimplementations are illustrative and that the scope of the disclosure isnot limited to them. Many variations, modifications, additions, andimprovements are possible. More generally, implementations in accordancewith the present disclosure have been described in the context ofparticular implementations. Functionality may be separated or combinedin blocks differently in various embodiments of the disclosure ordescribed with different terminology. These and other variations,modifications, additions, and improvements may fall within the scope ofthe disclosure as defined in the claims that follow.

1. (canceled)
 2. An apparatus for dialogue guidance, the apparatuscomprising: at least one memory; and at least one processor, the atleast one processor configured to: store a history of interactionsinvolving a plurality of participants of the interactions; analyze thehistory of interactions to identify at least one sentiment expressed byat least a subset of the plurality of participants through at least asubset of the interactions; edit content of an excerpt of dialogue basedon the at least one sentiment expressed by at least a subset of theplurality of participants to generate edited content for the excerpt ofdialogue; and provide an indication of the edited content for theexcerpt of dialogue.
 3. The apparatus of claim 2, wherein the content ofthe excerpt of dialogue includes a string of text, and wherein, to editthe content to generate the edited content for the excerpt of dialogue,the at least one processor is configured to edit the string of textbased on the at least one sentiment expressed by at least the subset ofthe plurality of participants to generate an edited string of text. 4.The apparatus of claim 2, wherein the content of the excerpt of dialogueincludes a spoken dialogue, and wherein, to edit the content to generatethe edited content for the excerpt of dialogue, the at least oneprocessor is configured to edit the spoken dialogue based on the atleast one sentiment expressed by at least the subset of the plurality ofparticipants to generate an edited spoken dialogue.
 5. The apparatus ofclaim 2, the at least one processor configured to: edit the content ofthe excerpt of dialogue to increase a likelihood of persuasiveness to atleast the subset of the plurality of participants based on the at leastone sentiment to generate the edited content for the excerpt ofdialogue.
 6. The apparatus of claim 2, the at least one processorconfigured to: edit the content of the excerpt of dialogue based on theat least one sentiment without changing a topic of the excerpt ofdialogue to generate the edited content for the excerpt of dialogue. 7.The apparatus of claim 2, the at least one processor configured to:transmit the edited content for the excerpt of dialogue to a recipientdevice at a specified time to provide the edited content for the excerptof dialogue.
 8. The apparatus of claim 2, wherein, to analyze thehistory of interactions to identify at least one sentiment, the at leastone processor is configured to analyze at least one of a gaze of atleast one of the plurality of participants, a respiratory response ofthe at least one of the plurality of participants, an audible responsefrom the at least one of the plurality of participants, a disposition ofthe at least one of the plurality of participants, or a facialexpression of the at least one of the plurality of participants.
 9. Theapparatus of claim 2, wherein the at least one sentiment includes atleast one disposition.
 10. The apparatus of claim 2, wherein, to analyzethe history of interactions to identify at least one sentiment, the atleast one processor is configured to analyze the history of interactionsaccording to at least one rule for communication.
 11. The apparatus ofclaim 2, the at least one processor configured to: provide the editedcontent for the excerpt of dialogue as an audio clip to provide theedited content for the excerpt of dialogue.
 12. The apparatus of claim2, the at least one processor configured to: provide the edited contentfor the excerpt of dialogue as a text string to provide the editedcontent for the excerpt of dialogue.
 13. The apparatus of claim 2, theat least one processor configured to: use at least one machine learningmodel to analyze the history of interactions to identify the at leastone sentiment expressed by at least the subset of the plurality ofparticipants through at least a subset of the interactions.
 14. Theapparatus of claim 13, the at least one processor configured to:identify at least one reaction to the edited content for the excerpt ofdialogue from at least one of the plurality of participants; and updatethe machine learning model based on the at least one reaction and on theedited content for the excerpt of dialogue.
 15. The apparatus of claim2, the at least one processor configured to: identify at least onereaction to the edited content for the excerpt of dialogue from at leastone of the plurality of participants; edit the edited content of theexcerpt of dialogue further based on the at least one reaction togenerate secondary edited content for the excerpt of dialogue; andprovide the secondary edited content for the excerpt of dialogue.
 16. Amethod for dialogue guidance, the method comprising: storing a historyof interactions involving a plurality of participants of theinteractions; analyzing the history of interactions to identify at leastone sentiment expressed by at least a subset of the plurality ofparticipants through at least a subset of the interactions; editingcontent of an excerpt of dialogue based on the at least one sentimentexpressed by at least a subset of the plurality of participants togenerate edited content for the excerpt of dialogue; and providing anindication of the edited content for the excerpt of dialogue.
 17. Themethod of claim 16, wherein editing the content of the excerpt ofdialogue based on the at least one sentiment to generate the editedcontent for the excerpt of dialogue includes editing the content of theexcerpt of dialogue to increase a likelihood of persuasiveness to atleast the subset of the plurality of participants based on the at leastone sentiment.
 18. The method of claim 16, wherein editing the contentof the excerpt of dialogue based on the at least one sentiment togenerate the edited content for the excerpt of dialogue includes editingthe content of the excerpt of dialogue based on the at least onesentiment without changing a topic of the excerpt of dialogue.
 19. Themethod of claim 16, wherein analyzing the history of interactions toidentify the at least one sentiment includes using at least one machinelearning model to analyze the history of interactions to identify the atleast one sentiment.
 20. The method of claim 19, further comprising:identifying at least one reaction to the edited content for the excerptof dialogue from at least one of the plurality of participants; andupdating the at least one machine learning model based on the at leastone reaction and on the edited content for the excerpt of dialogue. 21.The method of claim 16, further comprising: identifying at least onereaction to the edited content for the excerpt of dialogue from at leastone of the plurality of participants; editing the edited content of theexcerpt of dialogue further based on the at least one reaction togenerate secondary edited content for the excerpt of dialogue; andproviding the secondary edited content for the excerpt of dialogue. 22.A non-transitory, computer-readable storage medium, having embodiedthereon instructions executable by one or more processors to perform amethod for dialogue guidance, the method comprising: storing a historyof interactions involving a plurality of participants of theinteractions; analyzing the history of interactions to identify at leastone sentiment expressed by at least a subset of the plurality ofparticipants through at least a subset of the interactions; editingcontent of an excerpt of dialogue based on the at least one sentimentexpressed by at least a subset of the plurality of participants togenerate edited content for the excerpt of dialogue; and providing anindication of the edited content for the excerpt of dialogue.